Recurrent ANN based AGC of a two-area power system with DFIG based wind turbines considering asynchronous tie-lines

Modern power systems are large and complex with growing trends to integrate wind energy to the grid. The penetration of wind energy has motivated researchers to investigate the dynamic participation of doubly fed induction generators (DFIG) based wind turbines in automatic generation control (AGC) besides conventional generators. Power system is highly non-linear and complex. However, with dynamic participation of DFIG, the AGC problem becomes more complex. Under such conditions classical AGC are not suitable. Therefore, a new non-linear recurrent artificial neural network (ANN) based regulator for solution of AGC problem is proposed in this paper. The proposed AGC regulator is trained for a wide range of operating conditions and load changes using an off-line data set generated from the most accurate solution methodology of the power system. The back propagation-through time-algorithm is used as ANN learning rule. A two-area power system connected via asynchronous tie-lines with dynamic participation from DFIG based wind turbines in presence of system non-linearity such as governor dead-band is considered to demonstrate the effectiveness of the proposed AGC regulator and compared with that obtained using conventional PI, under wide range of operating conditions and area load disturbances.